Head of Engineering – GCP AI/ML, GenAI

This is a fully remote position, open to applicants in United States.

📋 Description

• Spearhead the strategy and execution for migrating large-scale data platforms from AWS to GCP.

• Design and implement enterprise GCP data platforms, including Data Lake and Lakehouse architectures, along with Bronze, Silver, and Gold/Atomic data layers.

• Develop and execute AI/ML and Generative AI solutions on GCP by leveraging Vertex AI and other related GCP-native services.

• Build production-quality machine learning pipelines for preparation, training, validation, evaluation, deployment, monitoring, retraining, and lifecycle management.

• Create GenAI and RAG solutions encompassing enterprise search, document intelligence, AI assistants, summarization, semantic search, embeddings, vector search, and knowledge management applications.

• Implement MLOps practices including model versioning, experiment tracking, validation, automated testing, deployment approvals, rollback strategies, and environment promotion.

• Set up monitoring for model performance, data drift, latency, reliability, inference costs, response quality, retrieval accuracy, hallucination, and prompt injection risks.

• Ensure responsible AI practices, along with data privacy, security, governance, access control, auditability, and human review processes.

• Analyze AWS platforms and align S3, Glue, Redshift, Athena, Step Functions, DMS, and Lake Formation workloads with GCP services.

• Architect real-time data pipelines using Pub/Sub, Dataflow/Apache Beam, BigQuery, and Cloud Storage.

• Design and implement batch and streaming ETL/ELT processes, CDC, transformation, orchestration, and data processing pipelines utilizing Python, PySpark, SQL, dbt, BigQuery, Cloud Composer/Airflow, and Dataflow.

• Define migration phases, technical dependencies, risks, rollback strategies, target-state architectures, and modernization opportunities.

• Develop enterprise data models, including dimensional, normalized, and denormalized structures, as well as multi-tenant data models, along with BigQuery partitioning and clustering strategies.

• Establish data governance, data quality, lineage, metadata management, ownership, IAM, encryption, service accounts, network security, and data access policies.

• Lead Terraform-based infrastructure automation, CI/CD, testing, deployment, and provisioning across Dev, QA, UAT, and Production environments.

• Optimize BigQuery, Dataflow, Spark, Cloud Storage, streaming workloads, performance, SLAs, and cloud expenditure.

• Mentor and lead Data Engineers, Senior Data Engineers, and Technical Leads; provide technical guidance, conduct architecture and code reviews, and outline roadmaps.

• Serve as the primary technical liaison for US-based stakeholders while collaborating with Business, Product, Data Science, BI, DevOps, Security, and Analytics teams.

• Translate business requirements into scalable technical solutions and clearly communicate architecture decisions, risks, dependencies, timelines, trade-offs, and KPIs.

⛳️ Requirements

• Over 15 years of experience in GCP Data Engineering, Data Architecture, Cloud Engineering, AI/ML Engineering, or similar technology leadership roles.

• More than 5 years of robust hands-on experience in GCP Data Engineering.

• At least 3 years of significant hands-on experience with AI/ML and Generative AI.

• Demonstrated success in delivering AWS-to-GCP migration projects.

• Strong experience in designing enterprise Data Lake and Lakehouse platforms on GCP.

• Extensive hands-on experience with BigQuery, Google Cloud Storage, Dataflow, Pub/Sub, Cloud Composer, Dataproc, IAM, and Terraform.

• Experience in migrating AWS data workloads, pipelines, and platforms to GCP.

• In-depth knowledge of AWS and GCP service mapping, migration patterns, modernization strategies, and cloud architecture best practices.

• Experience in designing, building, and deploying AI/ML solutions on GCP utilizing Vertex AI.

• Hands-on experience with Generative AI, LLM-based applications, RAG architectures, embeddings, vector search, prompt engineering, and enterprise AI assistants.

• Strong understanding of MLOps, encompassing model training, model registry, CI/CD/CT, model deployment, monitoring, retraining, governance, and rollback strategies.

• Experience in implementing secure and responsible AI solutions, including data privacy, model evaluation, access controls, auditability, and governance frameworks.

• Expert-level SQL skills along with strong capabilities in Python and PySpark.

• Solid experience in data modeling, data warehousing, batch processing, and real-time data engineering.

• Familiarity with Terraform, Git, GitHub, Cloud Build, CI/CD pipelines, and infrastructure automation.

• Experience in managing and mentoring data engineering and cross-functional technical teams.

• Excellent communication skills, with a background in working with US-based stakeholders.

• Google Cloud Professional Data Engineer certification is preferred.

• Google Cloud Professional Machine Learning Engineer certification is preferred.

• Experience with Vertex AI Agent Builder, Vertex AI Search, Gemini models on Vertex AI, or enterprise Generative AI platforms is preferred.

• Familiarity with dbt, Apache Airflow, Kafka, Apache Spark, Kubernetes, Cloud Run, and API-driven architectures is preferred.

• Experience with Dataplex, Data Catalog, data lineage, metadata management, data governance, master data management, and data quality frameworks is preferred.

• Experience supporting enterprise or regulated environments with stringent data privacy, security, compliance, audit, and governance requirements is preferred.

• Required technical skills include Python, PyTorch, TensorFlow, Scikit-learn, NLP, Deep Learning, machine learning algorithms, Generative AI, LLMs, GPT, Gemini, Claude, Llama, prompt engineering, fine-tuning, RAG, embeddings, vector databases, semantic search, hybrid search, reranking, LangChain, LlamaIndex, LangGraph, Hugging Face, Transformers, AI agents, agentic workflows, tool/function calling, multi-agent systems, MCP, MLflow, Kubeflow, model registry, model deployment, monitoring, CI/CD, Vertex AI, Vertex AI Studio, Vertex AI Pipelines, model garden, vector search, FastAPI, Flask, REST APIs, SQL, Docker, Kubernetes, GCP, AWS, Azure, BigQuery, Dataflow, Spark, Databricks, Data Lakes, advanced Python, expert SQL, PySpark, Apache Spark, ETL, ELT, CDC, batch & streaming, event-driven architecture, data pipeline development & optimization, enterprise Data Lake/Lakehouse, medallion architecture, data warehousing, data modeling, dimensional modeling, multi-tenant modeling, schema-on-read/schema-on-write, dbt, Apache Airflow/Cloud Composer, Dataproc, Dataflow/Apache Beam, AWS Glue, Redshift, EMR, Lambda, Kinesis, Athena, CloudWatch, GCS, AWS S3, Pub/Sub, Dataproc, Dataplex, Data Catalog, Terraform, Git/GitHub, Cloud Build, Infrastructure as Code, data lineage, metadata management, data quality, monitoring, and OpenLineage.

🏝️ Benefits

• Flexible remote work options.

• Opportunity to engage with global customers.

• Collaborative and innovation-driven work culture.

• Continuous learning and certification opportunities.

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